dell-ai

name	dell-ai
description	Skill for interacting with Dell Enterprise Hub (DEH) - discovering models, exploring hardware platforms, generating deployment snippets, etc.

Dell AI

dell-ai interacts with the Dell Enterprise Hub (DEH) — discovering models, exploring hardware platforms, generating deployment snippets, managing app catalog entries, checking system compatibility, etc.

When to use

• User wants to find AI models available on Dell Enterprise Hub
• User wants to find AI models available on DEH for their hardware platform
• User needs to check which Dell hardware platforms support a given model
• User wants to generate Docker or Kubernetes deployment commands for a model and then run it.
• User wants a goodput-optimized deployment snippet (balanced, long-context, high-concurrency, performance).
• User wants to explore or deploy applications from the Dell app catalog
• User needs to check system compatibility with Dell AI platforms
• User asks about Dell AI, Dell Enterprise Hub, or DEH
• User wants to authenticate with Dell AI using a Hugging Face token
• User wants to check their system info or check system compatibility against Dell AI validated configs

Installation

uv pip install dell-ai

Authentication

Uses a Hugging Face token. Auto-loads from HF token cache, or accepts one directly.

from dell_ai import DellAIClient

client = DellAIClient()                        # Uses cached HF token
client = DellAIClient(token="your_hf_token")   # Or explicit token

client.is_authenticated()  # Returns bool
client.get_user_info()     # Returns dict with name, email, orgs, etc.

Models

Search and list models

list_models returns List[str] of IDs. search_models accepts the same filters but returns full Model objects.

# List model IDs (all filters are optional and combinable)
models = client.list_models(
    query="llama",                    # Search name/description
    multimodal=True,                  # True for multimodal, False for text-only
    min_size=7000,                    # Min params in millions
    max_size=70000,                   # Max params in millions
    license_filter="apache",          # License substring match
    platform_id="xe9680-nvidia-h200", # Only models compatible with this platform
)

# Full Model objects with same filters
results = client.search_models(query="llama", multimodal=True)
# Model fields: repo_name, description, license, size, is_multimodal,
#               has_system_prompt, creator_type, status, configs_deploy

Get model details and compatible platforms

# Returns Model object
model = client.get_model("meta-llama/Llama-4-Maverick-17B-128E-Instruct")

platforms = client.get_compatible_platforms("google/gemma-3-27b-it")
# Returns List[PlatformCompatibility] with platform_id and configs (List[ModelConfig])
for p in platforms:
    print(p.platform_id, [c.model_dump() for c in p.configs])

Check model access (gated repos)

# Returns True, or raises GatedRepoAccessError / AuthenticationError
client.check_model_access("meta-llama/Llama-4-Maverick-17B-128E-Instruct")

Get model Deployment Snippets

Automatically validates model access and model-platform compatibility.

Provide either num_gpus (manual sizing) or goodput (server-optimized) — the two are mutually exclusive, and exactly one is required.

# Basic deployment snippet with manual GPU sizing
snippet = client.get_deployment_snippet(
    model_id="meta-llama/Llama-4-Maverick-17B-128E-Instruct",
    platform_id="xe9680-nvidia-h200",
    engine="docker",       # "docker" or "kubernetes"
    num_gpus=8,
    num_replicas=1,
)
print(snippet)  # Ready-to-use docker command or k8s manifest

# Goodput-optimized: DEH returns the GPU count and optimized params for the
# scenario. Omit num_gpus when using goodput.
snippet = client.get_deployment_snippet(
    model_id="nvidia/MiniMax-M2.7-NVFP4",
    platform_id="xe9680-nvidia-h200",
    engine="docker",
    goodput="balanced",    # balanced | long-context | high-concurrency | performance
)

Not every (model, platform, scenario) combination has an optimized config; the API is the source of truth and raises ResourceNotFoundError with a message like No optimized config for "balanced" scenario on this SKU. when it doesn't.

Goodput scenarios reference data

Global, static reference data: scenario definitions, SLO field docs, and SLO targets per SKU. Cached on disk after the first call.

ref = client.get_goodput_scenarios()
# GoodputReference fields:
#   scenarios            -> List[Scenario] (id, label, description)
#   slo_field_descriptions -> Dict[str, str]
#   slos_by_sku          -> Dict[SkuId, Dict[scenario, Slo]] (sparse)
# Slo fields: max_model_context, virtual_users, input_tokens [min,max], output_tokens [min,max]

slo = ref.slos_by_sku.get("xe9680-nvidia-h100", {}).get("balanced")

Platforms

platforms = client.list_platforms()  # Returns List[str] of SKU IDs

platform = client.get_platform("xe9680-nvidia-h200")
# Fields: id, name, platform_type, vendor, accelerator_type, accelerator,
#   gpuram, gpuinterconnect, totalgpucount, product_name

sys_infos = client.get_platform_system_info("xe9680-nvidia-h200")  # List[SystemInfo]

Applications

List and inspect apps

apps = client.list_apps()  # Returns List[str] of app names

app = client.get_app("openwebui")
# App fields: id, name, license, description, features, instructions,
#   tags, recommendedModels, components

Explore configurable parameters

for component in app.components:
    for param in component.config:
        print(f"  {param.name}: {param.helmPath} ({param.type}), default={param.default}")
    for secret in component.secrets:
        print(f"  [secret] {secret.name}: {secret.helmPath}")

Generate app deployment snippet

Each config item requires: helmPath, type ("string", "boolean", "number", or "json"), and value.

config = [
    {"helmPath": "main.config.storageClassName", "type": "string", "value": "gp2"},
    {"helmPath": "main.config.enableOpenAI", "type": "boolean", "value": True},
]
snippet = client.get_app_snippet("openwebui", config)
print(snippet)  # Helm install command

System Utilities (CLI only, Linux)

dell-ai utils describe-system              # Get system info as JSON
dell-ai utils describe-system -o out.json  # Save to file
dell-ai utils describe-system | jq         # Pretty-print
dell-ai utils check-system                 # Check compatibility against Dell AI validated configs

Requires: lscpu, lspci, lsblk, dmidecode. For GPUs: nvidia-smi, nvidia-ctk. For K8s: kubectl.

Exceptions

All in dell_ai.exceptions:

• AuthenticationError — Invalid or missing token
• GatedRepoAccessError — No access to a gated model repository
• ResourceNotFoundError — Model, platform, or app not found
• ValidationError — Invalid parameters (may include valid_values)
• APIError — General API errors